#gptai — Public Fediverse posts

Navneet Alang, a writer and cultural critic based in Toronto, coherently exposes the risk of AI falling prey to hype. The article will take time to read. It may be worth being patient. It covers almost all the current known trends of AI. I am highlighting a few that appeals to me.

What we call AI at the moment is mostly concerned with LLMs (large Language Models), or big language models. The models learn how tokens connect to one another and, over time, learn context, such as where a word might appear, in what order, and so on. Because LLMs are essentially looking into vast quantities of data patterns and determining how they connect to one another, they can frequently provide quite reasonable-sounding claims that are incorrect, incoherent, or simply bizarre.

The author then discusses the socio-political factors that influence technology implementation. This is the most interesting aspect to me. AI and machine learning's ability to evaluate millions of parameters at once may and extract patterns from data far trump humans' ability to parse specific types of altruistic use cases which can be reduced to data. It is not appropriate to believe that every problem can be solved or alleviated through technological interventions, often without considering the underlying social, political, or economic complexities. This is called techno-solutionism. If and when change occurs, it will be largely driven by political will, resources, and a battle of competing ideologies and interests that go beyond emergent technologies like AI and Machine Learning. The majority of the world's issues are not the result of a lack of intelligence or processing capability. Some of the answers to these challenges appear to be straightforward. However, the improvements are difficult to apply due to social and political pressures, rather than a lack of insight, thinking, originality, or technology.

Technology is primarily a tool. If one has a task to complete, technology can assist to do it. However, there are some key technologies, such as shelter, the printing press, the nuclear bomb or rocket and the internet, that almost redefined the world, changing our perception of both ourselves and reality. As mentioned before, the promise of AI resides in dealing with data sets on a scale that humans cannot handle. Pattern recognition machines used in biology or physics are likely to produce exciting and helpful results. Otherwise, as per author, AI applications are mostly quotidian. AI will not create a huge new universe, but rather, depending on our perspective, make what is already there slightly more efficient or deepen and consolidate the structure of the present. Yes, some aspects of our job may be easier, but it appears that those automated duties will eventually become part of more work.

#AI #OpenAI #ChatGPT #ZeroTrustInformation #LargeLanguageModels #LLM #GPTAI #TechSolutionism

Navneet Alang, a writer and cultural critic based in Toronto, coherently exposes the risk of AI falling prey to hype. The article will take time to read. It may be worth being patient. It covers almost all the current known trends of AI. I am highlighting a few that appeals to me.

What we call AI at the moment is mostly concerned with LLMs (large Language Models), or big language models. The models learn how tokens connect to one another and, over time, learn context, such as where a word might appear, in what order, and so on. Because LLMs are essentially looking into vast quantities of data patterns and determining how they connect to one another, they can frequently provide quite reasonable-sounding claims that are incorrect, incoherent, or simply bizarre.

The author then discusses the socio-political factors that influence technology implementation. This is the most interesting aspect to me. AI and machine learning's ability to evaluate millions of parameters at once may and extract patterns from data far trump humans' ability to parse specific types of altruistic use cases which can be reduced to data. It is not appropriate to believe that every problem can be solved or alleviated through technological interventions, often without considering the underlying social, political, or economic complexities. This is called techno-solutionism. If and when change occurs, it will be largely driven by political will, resources, and a battle of competing ideologies and interests that go beyond emergent technologies like AI and Machine Learning. The majority of the world's issues are not the result of a lack of intelligence or processing capability. Some of the answers to these challenges appear to be straightforward. However, the improvements are difficult to apply due to social and political pressures, rather than a lack of insight, thinking, originality, or technology.

Technology is primarily a tool. If one has a task to complete, technology can assist to do it. However, there are some key technologies, such as shelter, the printing press, the nuclear bomb or rocket and the internet, that almost redefined the world, changing our perception of both ourselves and reality. As mentioned before, the promise of AI resides in dealing with data sets on a scale that humans cannot handle. Pattern recognition machines used in biology or physics are likely to produce exciting and helpful results. Otherwise, as per author, AI applications are mostly quotidian. AI will not create a huge new universe, but rather, depending on our perspective, make what is already there slightly more efficient or deepen and consolidate the structure of the present. Yes, some aspects of our job may be easier, but it appears that those automated duties will eventually become part of more work.

#AI #OpenAI #ChatGPT #ZeroTrustInformation #LargeLanguageModels #LLM #GPTAI #TechSolutionism

Navneet Alang, a writer and cultural critic based in Toronto, coherently exposes the risk of AI falling prey to hype. The article will take time to read. It may be worth being patient. It covers almost all the current known trends of AI. I am highlighting a few that appeals to me.

What we call AI at the moment is mostly concerned with LLMs (large Language Models), or big language models. The models learn how tokens connect to one another and, over time, learn context, such as where a word might appear, in what order, and so on. Because LLMs are essentially looking into vast quantities of data patterns and determining how they connect to one another, they can frequently provide quite reasonable-sounding claims that are incorrect, incoherent, or simply bizarre.

The author then discusses the socio-political factors that influence technology implementation. This is the most interesting aspect to me. AI and machine learning's ability to evaluate millions of parameters at once may and extract patterns from data far trump humans' ability to parse specific types of altruistic use cases which can be reduced to data. It is not appropriate to believe that every problem can be solved or alleviated through technological interventions, often without considering the underlying social, political, or economic complexities. This is called techno-solutionism. If and when change occurs, it will be largely driven by political will, resources, and a battle of competing ideologies and interests that go beyond emergent technologies like AI and Machine Learning. The majority of the world's issues are not the result of a lack of intelligence or processing capability. Some of the answers to these challenges appear to be straightforward. However, the improvements are difficult to apply due to social and political pressures, rather than a lack of insight, thinking, originality, or technology.

Technology is primarily a tool. If one has a task to complete, technology can assist to do it. However, there are some key technologies, such as shelter, the printing press, the nuclear bomb or rocket and the internet, that almost redefined the world, changing our perception of both ourselves and reality. As mentioned before, the promise of AI resides in dealing with data sets on a scale that humans cannot handle. Pattern recognition machines used in biology or physics are likely to produce exciting and helpful results. Otherwise, as per author, AI applications are mostly quotidian. AI will not create a huge new universe, but rather, depending on our perspective, make what is already there slightly more efficient or deepen and consolidate the structure of the present. Yes, some aspects of our job may be easier, but it appears that those automated duties will eventually become part of more work.

#AI #OpenAI #ChatGPT #ZeroTrustInformation #LargeLanguageModels #LLM #GPTAI #TechSolutionism

Navneet Alang, a writer and cultural critic based in Toronto, coherently exposes the risk of AI falling prey to hype. The article will take time to read. It may be worth being patient. It covers almost all the current known trends of AI. I am highlighting a few that appeals to me.

What we call AI at the moment is mostly concerned with LLMs (large Language Models), or big language models. The models learn how tokens connect to one another and, over time, learn context, such as where a word might appear, in what order, and so on. Because LLMs are essentially looking into vast quantities of data patterns and determining how they connect to one another, they can frequently provide quite reasonable-sounding claims that are incorrect, incoherent, or simply bizarre.

The author then discusses the socio-political factors that influence technology implementation. This is the most interesting aspect to me. AI and machine learning's ability to evaluate millions of parameters at once may and extract patterns from data far trump humans' ability to parse specific types of altruistic use cases which can be reduced to data. It is not appropriate to believe that every problem can be solved or alleviated through technological interventions, often without considering the underlying social, political, or economic complexities. This is called techno-solutionism. If and when change occurs, it will be largely driven by political will, resources, and a battle of competing ideologies and interests that go beyond emergent technologies like AI and Machine Learning. The majority of the world's issues are not the result of a lack of intelligence or processing capability. Some of the answers to these challenges appear to be straightforward. However, the improvements are difficult to apply due to social and political pressures, rather than a lack of insight, thinking, originality, or technology.

Technology is primarily a tool. If one has a task to complete, technology can assist to do it. However, there are some key technologies, such as shelter, the printing press, the nuclear bomb or rocket and the internet, that almost redefined the world, changing our perception of both ourselves and reality. As mentioned before, the promise of AI resides in dealing with data sets on a scale that humans cannot handle. Pattern recognition machines used in biology or physics are likely to produce exciting and helpful results. Otherwise, as per author, AI applications are mostly quotidian. AI will not create a huge new universe, but rather, depending on our perspective, make what is already there slightly more efficient or deepen and consolidate the structure of the present. Yes, some aspects of our job may be easier, but it appears that those automated duties will eventually become part of more work.

#AI #OpenAI #ChatGPT #ZeroTrustInformation #LargeLanguageModels #LLM #GPTAI #TechSolutionism

Navneet Alang, a writer and cultural critic based in Toronto, coherently exposes the risk of AI falling prey to hype. The article will take time to read. It may be worth being patient. It covers almost all the current known trends of AI. I am highlighting a few that appeals to me.

What we call AI at the moment is mostly concerned with LLMs (large Language Models), or big language models. The models learn how tokens connect to one another and, over time, learn context, such as where a word might appear, in what order, and so on. Because LLMs are essentially looking into vast quantities of data patterns and determining how they connect to one another, they can frequently provide quite reasonable-sounding claims that are incorrect, incoherent, or simply bizarre.

The author then discusses the socio-political factors that influence technology implementation. This is the most interesting aspect to me. AI and machine learning's ability to evaluate millions of parameters at once may and extract patterns from data far trump humans' ability to parse specific types of altruistic use cases which can be reduced to data. It is not appropriate to believe that every problem can be solved or alleviated through technological interventions, often without considering the underlying social, political, or economic complexities. This is called techno-solutionism. If and when change occurs, it will be largely driven by political will, resources, and a battle of competing ideologies and interests that go beyond emergent technologies like AI and Machine Learning. The majority of the world's issues are not the result of a lack of intelligence or processing capability. Some of the answers to these challenges appear to be straightforward. However, the improvements are difficult to apply due to social and political pressures, rather than a lack of insight, thinking, originality, or technology.

Technology is primarily a tool. If one has a task to complete, technology can assist to do it. However, there are some key technologies, such as shelter, the printing press, the nuclear bomb or rocket and the internet, that almost redefined the world, changing our perception of both ourselves and reality. As mentioned before, the promise of AI resides in dealing with data sets on a scale that humans cannot handle. Pattern recognition machines used in biology or physics are likely to produce exciting and helpful results. Otherwise, as per author, AI applications are mostly quotidian. AI will not create a huge new universe, but rather, depending on our perspective, make what is already there slightly more efficient or deepen and consolidate the structure of the present. Yes, some aspects of our job may be easier, but it appears that those automated duties will eventually become part of more work.

#AI #OpenAI #ChatGPT #ZeroTrustInformation #LargeLanguageModels #LLM #GPTAI #TechSolutionism